Application of Chemometrics for Spectral Resolving and Determination of Three Analgesics in Water Samples

2020 ◽  
Vol 103 (1) ◽  
pp. 257-264 ◽  
Author(s):  
Ali M Yehia ◽  
Heba T Elbalkiny ◽  
Safa’a M Riad ◽  
Yasser S Elsaharty
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Water Samples ◽  
Principal Component ◽  
Multivariate Curve Resolution ◽  
Mean Square ◽  
Qualitative And Quantitative

Abstract Background: Chemometrics is a discipline that allows the spectral resolution of drugs in a complicated matrix (e.g., environmental water samples) as an alternative to chromatographic methods. Objective: Three analgesics were traced in wastewater samples with simple and cost-effective multivariate approaches using spectrophotometric data. Methods and Results: Four chemometric approaches were applied for the simultaneous determination of diclofenac, paracetamol, and ibuprofen. Partial least squares (PLS), principal component regression (PCR), artificial neural networks (ANN), and multivariate curve resolution (MCR)–alternating least squares (ALS) were selected. The presented methods were compared and validated for their qualitative and quantitative analyses. Moreover, statistical comparison between the results obtained by the proposed methods and the official methods showed no significant differences. Conclusions: The proposed multivariate calibrations were accurate and specific for quantitative analysis of the studied components. MCR-ALS is the only method that has the capacity for both the quantitative and qualitative analysis of the studied drugs. Highlights: Four chemometric approaches were used for analysis of severally overlapped ternary mixture of three analgesics. The analytical performance of PCR, PLS, MCR-ALS, and ANN was compared and validated in terms of root mean square error of calibration (RMSEC), SE of prediction, and recoveries. ANN gave the highest predicted concentrations with the lowest RMSEC and root mean square error of prediction. MCR-ALS has the capacity for both qualitative and quantitative measurement. The methods have been effectively applied for real samples and compared to official methods.

scholarly journals Utilization of Picolinaldehyde-4-phenyl-3-thiosemicarbazone in Sodium Dodecylsulfate Micelles for the Spectrophotometric Determination of Iron, Vanadium, and Cobalt Following Partial Least-Squares Regression Analysis

2009 ◽  
Vol 92 (1) ◽  
pp. 248-256
Author(s):  
Aamna Balouch ◽  
Najma Memon ◽  
Muhammad I Bhanger ◽  
Muhammad Y Khuhawar
Keyword(s):  
Root Mean Square Error ◽  
Metal Ions ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Sodium Dodecylsulfate ◽  
Mean Square ◽  
Least Squares Regression

Abstract Partial least-squares regression was applied for the simultaneous determination of iron, vanadium, and cobalt after complexation with picolinaldehyde-4-phenyl-3-thiosemicarbazone (PAPT) in the presence of anionic sodium dodecylsulfate (SDS) micelles. These 3 complexed metal ions exhibited overlapping spectra in the 390510 nm region with a maximum absorbance at 415 nm at pH 3.0 and enhanced absorbance in the presence of SDS. The data for the simultaneous determination of these metal ions were analyzed using a simple partial least-squares (SIMPLS) algorithm. Formation constants (log Kf) were found to be 4.65, 3.29, and 4.85 for PAPT complexes of Fe, V, and Co, respectively, and the detection limits for Fe, V, and Co were 0.013, 0.002, and 0.010 g/mL, respectively. Common anions and cations did not interfere with the proposed method. The method was validated by calculating root mean square error of cross-validation, root mean square error of calibration, and root mean square error of prediction and was applied to determine these 3 metal ions in real crude oil samples.

scholarly journals Rapid Quality Identification of Decoction Pieces of Crude and Processed Corydalis Rhizoma by Near-Infrared Spectroscopy Coupled with Chemometrics

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Weihao Zhu ◽  
Hao Hong ◽  
Zhihui Hong ◽  
Xianjie Kang ◽  
Weifeng Du ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Quantitative Model ◽  
Mean Square ◽  
Qualitative And Quantitative

In order to identify the quality of crude and processed Corydalis Rhizoma decoction pieces, the research established a simple, fast, reliable, and validated near-infrared qualitative and quantitative model combined with chemometrics. 51 batches of crude and 40 batches of processed Corydalis Rhizoma from the Zhejiang and Jiangsu provinces of China were collected and analyzed. Crude and processed Corydalis Rhizoma samples were crushed to obtain NIR spectra. The content of seven alkaloids in crude and processed Corydalis Rhizoma was determined by high-performance liquid chromatography (HPLC). Pretreatment methods were screened such as normalization methods, offset filtering methods, and smoothing. Combined with partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS), the qualitative and quantitative models of crude and processed Corydalis Rhizoma were established, and the correlation coefficient (R2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used as evaluation indexes. Tetrahydropalmatine was used as an example for screening pretreatment methods; the results showed that MSC combined with the second derivative and no smoothing and the model with the wavelength range of 10000–5000 cm−1 had the best predictive ability and applied to all seven alkaloid components. Among them, the correlation coefficients were all higher than 0.99, and RMSEC and RMSEP were all less than 1%. The qualitative and quantitative model of the seven alkaloids in Corydalis Rhizoma can effectively identify the crude and processed Corydalis Rhizoma and determine the content of the seven alkaloids. By studying the NIR qualitative and quantitative models of crude and processed Corydalis Rhizoma, we can achieve rapid discrimination and quantitative prediction of crude and processed Corydalis Rhizoma. These methods can greatly improve the efficiency of traditional Chinese medicine analysis and provide a strong scientific basis for the quality identification and control of traditional Chinese medicine.

scholarly journals A useful quantitative model for determination of enantiomeric composition of racemate praziquantel by ultraviolet spectroscopy combined with partial least squares and its application to praziquantel tablets

2018 ◽  
Vol 11 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Man Zhao ◽  
Ran Meng ◽  
Yifang Lu ◽  
Lingyun Hu ◽  
Na Sun ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Reference Value ◽  
Quantitative Model ◽  
Enantiomeric Composition ◽  
Mean Square ◽  
Validation Set

A simple and novel method has been proposed to determine the enantiomeric composition of racemate praziquantel (PZQ) by using the analysis of ultraviolet (UV) spectroscopy combined with partial least squares (PLS). This method does not rely on the use of expensive carbohydrates such as cyclodextrins, but on the use of inexpensive sucrose, which is equally effective as carbohydrate. PZQ has two enantiomers. Through measuring the slight difference in the UV spectral absorption of PZQ due to different interactions between its two enantiomers and sucrose, the enantiomeric composition was determined by a quantitative model based on PLS analysis. The model showed that the correlation coefficients of calibration set and validation set were 0.9971 and 0.9972, respectively. The root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) were 0.0167 and 0.0129, respectively. Then, the independent data of PZQ tablets were also used to test how well the quantitative model of PLS predicted the enantiomeric composition. The ratio of S-PZQ in tablet was 0.492, determined by high-performance liquid chromatography as the reference value. Six solutions of the tablet samples were prepared, and the ratios of S-PZQ in tablet samples in the validation set were predicted by the PLS model. Their relative errors with the reference value were not more than 4%. Therefore, the established model could be accurate and employed to predict the enantiomeric compositions of PZQ tablets.

scholarly journals Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control

2018 ◽  
Vol 11 (06) ◽  
pp. 1850034
Author(s):  
Hongxia Huang ◽  
Yuanyuan Lv ◽  
Xiaoyi Sun ◽  
Shuangshuang Fu ◽  
Xuefang Lou ◽  
...  
Keyword(s):  
Quality Control ◽  
Chinese Medicine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Uv Spectroscopy ◽  
Uv Spectrophotometry ◽  
Mean Square ◽  
Tannin Content

A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coefficient ([Formula: see text]) of 0.952, root mean square error of calibration (RMSEC) of 0.476[Formula: see text][Formula: see text]g/ml, root mean square error of validation (RMSEV) of 1.171[Formula: see text][Formula: see text]g/ml, and root mean square error of prediction (RMSEP) of 0.465[Formula: see text][Formula: see text]g/ml. Pattern recognition models using linear discriminant analysis (LDA) and [Formula: see text] nearest neighbor ([Formula: see text]-NN) classifiers based on UV spectrum could successfully classify different types of injections and different manufacturers. The established method to measure tannin content based on UV spectroscopy is simple, rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

2017 ◽  
Vol 71 (11) ◽  
pp. 2427-2436 ◽  
Author(s):  
Mi Lei ◽  
Long Chen ◽  
Bisheng Huang ◽  
Keli Chen
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Mean Square ◽  
Characteristic Spectrum

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

Determination of Loganin in Qiju Dihuang Pills by Near-Infrared Spectroscopy Technique

2013 ◽  
Vol 807-809 ◽  
pp. 1978-1983 ◽  
Author(s):  
Cai Xia Xie ◽  
Hai Yan Gong ◽  
Jian Ying Liu ◽  
Jing Wei Lei ◽  
Xiao Yan Duan ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Partial Least Square ◽  
Calibration Model ◽  
Mean Square

To establish a rapid analytical method for Loganin in Qiju Dihuang Pills (condensed) by Near-infrared Diffuse Reflectance Technique. Collecting NIR spectra by NIR Diffuse Reflectance Spectroscopy, the partial least square calibration model was built. The correlation coefficients (R2) and the root-mean-square error of cross-validation (RMSECV) were 0.99764 and 0.09340, respectively. In the external validation,coefficients of determination (r2) between NIRS and HPLC values was 0.97348,the root-mean-square error of prediction (RMSEP) was 0.08491. The results showed that the method was rapid, accurate, and could be applied to the fast determination of Loganin in Qiju Dihuang Pills (condensed).

scholarly journals Online NIR Analysis and Prediction Model for Synthesis Process of Ethyl 2-Chloropropionate

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Wei Zhang ◽  
Hang Song ◽  
Jing Lu ◽  
Wen Liu ◽  
Lirong Nie ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Squares Regression ◽  
Synthesis Process ◽  
Calibration Model ◽  
Mean Square ◽  
Least Squares Regression

Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R2) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean square error of correction (RMSEC) was 0.018105 mol/L. These values of PLSR and RMSEC could prove that the quantitative calibration model had good performance. Moreover, the root mean square error of prediction (RMSEP) of validation set was 0.036429 mol/L. The results were very similar to those of offline gas chromatographic analysis, which could prove the method was valid.

scholarly journals Online Measurement of Urea Concentration in Spent Dialysate during Hemodialysis

2004 ◽  
Vol 50 (1) ◽  
pp. 175-181 ◽  
Author(s):  
Jonathon T Olesberg ◽  
Mark A Arnold ◽  
Michael J Flanigan
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Squares Regression ◽  
Mean Square ◽  
Least Squares Regression ◽  
Net Analyte Signal ◽  
Analyte Signal

Abstract Background: We describe online optical measurements of urea in the effluent dialysate line during regular hemodialysis treatment of several patients. Monitoring urea removal can provide valuable information about dialysis efficiency. Methods: Spectral measurements were performed with a Fourier-transform infrared spectrometer equipped with a flow-through cell. Spectra were recorded across the 5000–4000 cm−1 (2.0–2.5 μm) wavelength range at 1-min intervals. Savitzky–Golay filtering was used to remove baseline variations attributable to the temperature dependence of the water absorption spectrum. Urea concentrations were extracted from the filtered spectra by use of partial least-squares regression and the net analyte signal of urea. Results: Urea concentrations predicted by partial least-squares regression matched concentrations obtained from standard chemical assays with a root mean square error of 0.30 mmol/L (0.84 mg/dL urea nitrogen) over an observed concentration range of 0–11 mmol/L. The root mean square error obtained with the net analyte signal of urea was 0.43 mmol/L with a calibration based only on a set of pure-component spectra. The error decreased to 0.23 mmol/L when a slope and offset correction were used. Conclusions: Urea concentrations can be continuously monitored during hemodialysis by near-infrared spectroscopy. Calibrations based on the net analyte signal of urea are particularly appealing because they do not require a training step, as do statistical multivariate calibration procedures such as partial least-squares regression.

Estimation of precision of determination of the Yarkovsky effect parameter based on real and model asteroid observations

2021 ◽  
pp. 170-174
Author(s):  
T.Yu. Galushina ◽  
◽  
O.N. Letner ◽  
O.M. Syusina ◽  
◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Observation Interval ◽  
Mean Square ◽  
Yarkovsky Effect ◽  
Effect Parameter ◽  
Large Observation

The paper presents the results of assessment definition precision of the Yarkovsky effect parameter A 2 for asteroids with small perihelion distances, known on epoch January 2021. It is shown that the observation interval has a significant effect on the precision of A 2. As the interval increases, the root mean square error of the parameter decreases. For asteroids (3200) Phaethon and (137924) 2000 BD19 with a large observation interval, an experiment was carried out to reduce the number of real observations. A decrease of the interval and number of observations leads to a loss in the precision of the parameter being determined. Modeling observations based on real ones with an increase in their precision showed that the root mean square error of the A 2 parameter decreases in proportion to the increase in the observation precision. The increase of interval due to model observations confirmed the conclusion about the inverse dependence of the A 2 uncertainty from number and interval of observations.

Assessment of Pareto Calibration, Stability, and Wavelength Selection

2003 ◽  
Vol 57 (3) ◽  
pp. 309-316 ◽  
Author(s):  
Kelly J. Anderson ◽  
John H. Kalivas
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Principal Component Regression ◽  
Principal Component ◽  
Data Sets ◽  
Mean Square ◽  
Wavelength Selection ◽  
Pareto Model ◽  
Improve Analysis

Recent work has shown that ridge regression (RR) is Pareto to partial least squares (PLS) and principal component regression (PCR) when the variance indicator Euclidian norm of the regression coefficients, ‖p̂‖, is plotted against the bias indicator root mean square error of calibration (RMSEC). Simplex optimization demonstrates that RR is Pareto for several other spectral data sets when ‖p̂‖ is used with RMSEC and the root mean square error of evaluation (RMSEE) as optimization criteria. From this investigation, it was observed that while RR is Pareto optimal, PLS and PCR harmonious models are near equivalent to harmonious RR models. Additionally, it was found that RR is Pareto robust, i.e., models formed at one temperature were then used to predict samples at another temperature. Wavelength selection is commonly performed to improve analysis results such that bias indicators RMSEC, RMSEE, root mean square error of validation, or root mean square error of cross-validation decrease using a subset of wavelengths. Just as critical to an analysis of selected wavelengths is an assessment of variance. Using wavelengths deemed optimal in a previous study, this paper reports on the variance/bias tradeoff. An approach that forms the Pareto model with a Pareto wavelength subset is suggested.

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